# Deconvolution/2D+

Deconvolution/2D+
You are encouraged to solve this task according to the task description, using any language you may know.

This task is a straightforward generalization of Deconvolution/1D to higher dimensions. For example, the one dimensional case would be applicable to audio signals, whereas two dimensions would pertain to images. Define the discrete convolution in ${\displaystyle {\mathit {d}}}$ dimensions of two functions

${\displaystyle H,F:\mathbb {Z} ^{d}\rightarrow \mathbb {R} }$

taking ${\displaystyle {\mathit {d}}}$-tuples of integers to real numbers as the function

${\displaystyle G:\mathbb {Z} ^{d}\rightarrow \mathbb {R} }$

also taking ${\displaystyle {\mathit {d}}}$-tuples of integers to reals and satisfying

${\displaystyle G(n_{0},\dots ,n_{d-1})=\sum _{m_{0}=-\infty }^{\infty }\dots \sum _{m_{d-1}=-\infty }^{\infty }F(m_{0},\dots ,m_{d-1})H(n_{0}-m_{0},\dots ,n_{d-1}-m_{d-1})}$

for all ${\displaystyle {\mathit {d}}}$-tuples of integers ${\displaystyle (n_{0},\dots ,n_{d-1})\in \mathbb {Z} ^{d}}$. Assume ${\displaystyle {\mathit {F}}}$ and ${\displaystyle {\mathit {H}}}$ (and therefore ${\displaystyle {\mathit {G}}}$) are non-zero over only a finite domain bounded by the origin, hence possible to represent as finite multi-dimensional arrays or nested lists ${\displaystyle {\mathit {f}}}$, ${\displaystyle {\mathit {h}}}$, and ${\displaystyle {\mathit {g}}}$.

For this task, implement a function (or method, procedure, subroutine, etc.) deconv to perform deconvolution (i.e., the inverse of convolution) by solving for ${\displaystyle {\mathit {h}}}$ given ${\displaystyle {\mathit {f}}}$ and ${\displaystyle {\mathit {g}}}$. (See Deconvolution/1D for details.)

• The function should work for ${\displaystyle {\mathit {g}}}$ of arbitrary length in each dimension (i.e., not hard coded or constant) and ${\displaystyle {\mathit {f}}}$ of any length up to that of ${\displaystyle {\mathit {g}}}$ in the corresponding dimension.
• The deconv function will need to be parameterized by the dimension ${\displaystyle {\mathit {d}}}$ unless the dimension can be inferred from the data structures representing ${\displaystyle {\mathit {g}}}$ and ${\displaystyle {\mathit {f}}}$.
• There may be more equations than unknowns. If convenient, use a function from a library that finds the best fitting solution to an overdetermined system of linear equations (as in the Multiple regression task). Otherwise, prune the set of equations as needed and solve as in the Reduced row echelon form task.
• Debug your solution using this test data, of which a portion is shown below. Be sure to verify both that the deconvolution of ${\displaystyle {\mathit {g}}}$ with ${\displaystyle {\mathit {f}}}$ is ${\displaystyle {\mathit {h}}}$ and that the deconvolution of ${\displaystyle {\mathit {g}}}$ with ${\displaystyle {\mathit {h}}}$ is ${\displaystyle {\mathit {f}}}$. Display the results in a human readable form for the three dimensional case only.

dimension 1:

h: [-8, 2, -9, -2, 9, -8, -2]
f: [ 6, -9, -7, -5]
g: [-48, 84, -16, 95, 125, -70, 7, 29, 54, 10]


dimension 2:

h: [
[-8, 1, -7, -2, -9, 4],
[4, 5, -5, 2, 7, -1],
[-6, -3, -3, -6, 9, 5]]
f: [
[-5, 2, -2, -6, -7],
[9, 7, -6, 5, -7],
[1, -1, 9, 2, -7],
[5, 9, -9, 2, -5],
[-8, 5, -2, 8, 5]]
g: [
[40, -21, 53, 42, 105, 1, 87, 60, 39, -28],
[-92, -64, 19, -167, -71, -47, 128, -109, 40, -21],
[58, 85, -93, 37, 101, -14, 5, 37, -76, -56],
[-90, -135, 60, -125, 68, 53, 223, 4, -36, -48],
[78, 16, 7, -199, 156, -162, 29, 28, -103, -10],
[-62, -89, 69, -61, 66, 193, -61, 71, -8, -30],
[48, -6, 21, -9, -150, -22, -56, 32, 85, 25]]


dimension 3:

h: [
[[-6, -8, -5, 9], [-7, 9, -6, -8], [2, -7, 9, 8]],
[[7, 4, 4, -6], [9, 9, 4, -4], [-3, 7, -2, -3]]]
f: [
[[-9, 5, -8], [3, 5, 1]],
[[-1, -7, 2], [-5, -6, 6]],
[[8, 5, 8],[-2, -6, -4]]]
g: [
[
[54, 42, 53, -42, 85, -72],
[45, -170, 94, -36, 48, 73],
[-39, 65, -112, -16, -78, -72],
[6, -11, -6, 62, 49, 8]],
[
[-57, 49, -23, 52, -135, 66],
[-23, 127, -58, -5, -118, 64],
[87, -16, 121, 23, -41, -12],
[-19, 29, 35, -148, -11, 45]],
[
[-55, -147, -146, -31, 55, 60],
[-88, -45, -28, 46, -26, -144],
[-12, -107, -34, 150, 249, 66],
[11, -15, -34, 27, -78, -50]],
[
[56, 67, 108, 4, 2, -48],
[58, 67, 89, 32, 32, -8],
[-42, -31, -103, -30, -23, -8],
[6, 4, -26, -10, 26, 12]]]


## C

Very tedious code: unpacks 2D or 3D matrix into a vector with padding, do 1D FFT, then pack result back into matrix.

#include <stdio.h>#include <stdlib.h>#include <math.h>#include <complex.h> double PI;typedef double complex cplx; void _fft(cplx buf[], cplx out[], int n, int step){	if (step < n) {		_fft(out, buf, n, step * 2);		_fft(out + step, buf + step, n, step * 2); 		for (int i = 0; i < n; i += 2 * step) {			cplx t = cexp(-I * PI * i / n) * out[i + step];			buf[i / 2]     = out[i] + t;			buf[(i + n)/2] = out[i] - t;		}	}} void fft(cplx buf[], int n){	cplx out[n];	for (int i = 0; i < n; i++) out[i] = buf[i];	_fft(buf, out, n, 1);} /* pad array length to power of two */cplx *pad_two(double g[], int len, int *ns){	int n = 1;	if (*ns) n = *ns;	else while (n < len) n *= 2; 	cplx *buf = calloc(sizeof(cplx), n);	for (int i = 0; i < len; i++) buf[i] = g[i];	*ns = n;	return buf;} void deconv(double g[], int lg, double f[], int lf, double out[], int row_len) {	int ns = 0;	cplx *g2 = pad_two(g, lg, &ns);	cplx *f2 = pad_two(f, lf, &ns); 	fft(g2, ns);	fft(f2, ns); 	cplx h[ns];	for (int i = 0; i < ns; i++) h[i] = g2[i] / f2[i];	fft(h, ns); 	for (int i = 0; i < ns; i++) {		if (cabs(creal(h[i])) < 1e-10) 			h[i] = 0;	} 	for (int i = 0; i > lf - lg - row_len; i--)		out[-i] = h[(i + ns) % ns]/32;	free(g2);	free(f2);} double* unpack2(void *m, int rows, int len, int to_len){	double *buf = calloc(sizeof(double), rows * to_len);	for (int i = 0; i < rows; i++)		for (int j = 0; j < len; j++)			buf[i * to_len + j] = ((double(*)[len])m)[i][j];	return buf;} void pack2(double * buf, int rows, int from_len, int to_len, void *out){	for (int i = 0; i < rows; i++)		for (int j = 0; j < to_len; j++)			((double(*)[to_len])out)[i][j] = buf[i * from_len + j] / 4;} void deconv2(void *g, int row_g, int col_g, void *f, int row_f, int col_f, void *out) {	double *g2 = unpack2(g, row_g, col_g, col_g);	double *f2 = unpack2(f, row_f, col_f, col_g); 	double ff[(row_g - row_f + 1) * col_g];	deconv(g2, row_g * col_g, f2, row_f * col_g, ff, col_g);	pack2(ff, row_g - row_f + 1, col_g, col_g - col_f + 1, out); 	free(g2);	free(f2);} double* unpack3(void *m, int x, int y, int z, int to_y, int to_z){	double *buf = calloc(sizeof(double), x * to_y * to_z);	for (int i = 0; i < x; i++)		for (int j = 0; j < y; j++) {			for (int k = 0; k < z; k++)				buf[(i * to_y + j) * to_z + k] =					((double(*)[y][z])m)[i][j][k];		}	return buf;} void pack3(double * buf, int x, int y, int z, int to_y, int to_z, void *out){	for (int i = 0; i < x; i++)		for (int j = 0; j < to_y; j++)			for (int k = 0; k < to_z; k++)				((double(*)[to_y][to_z])out)[i][j][k] =					buf[(i * y + j) * z + k] / 4;} void deconv3(void *g, int gx, int gy, int gz, void *f, int fx, int fy, int fz, void *out) {	double *g2 = unpack3(g, gx, gy, gz, gy, gz);	double *f2 = unpack3(f, fx, fy, fz, gy, gz); 	double ff[(gx - fx + 1) * gy * gz];	deconv(g2, gx * gy * gz, f2, fx * gy * gz, ff, gy * gz);	pack3(ff, gx - fx + 1, gy, gz, gy - fy + 1, gz - fz + 1, out); 	free(g2);	free(f2);} int main(){	PI = atan2(1,1) * 4;	double h[2][3][4] = {		{{-6, -8, -5,  9}, {-7, 9, -6, -8}, { 2, -7,  9,  8}},		{{ 7,  4,  4, -6}, { 9, 9,  4, -4}, {-3,  7, -2, -3}}	};	int hx = 2, hy = 3, hz = 4;	double f[3][2][3] = {	{{-9,  5, -8}, { 3,  5,  1}},				{{-1, -7,  2}, {-5, -6,  6}},				{{ 8,  5,  8}, {-2, -6, -4}} };	int fx = 3, fy = 2, fz = 3;	double g[4][4][6] = {		{	{ 54,  42,  53, -42,  85, -72}, { 45,-170,  94, -36,  48,  73},			{-39,  65,-112, -16, -78, -72}, {  6, -11,  -6,  62,  49,   8} },		{ 	{-57,  49, -23,   52, -135,  66},{-23, 127, -58,   -5, -118,  64},			{ 87, -16,  121,  23,  -41, -12},{-19,  29,   35,-148,  -11,  45} },		{	{-55, -147, -146, -31,  55,  60},{-88,  -45,  -28,  46, -26,-144},			{-12, -107,  -34, 150, 249,  66},{ 11,  -15,  -34,  27, -78, -50} },		{	{ 56,  67, 108,   4,  2,-48},{ 58,  67,  89,  32, 32, -8},			{-42, -31,-103, -30,-23, -8},{  6,   4, -26, -10, 26, 12}		}	};	int gx = 4, gy = 4, gz = 6; 	double h2[gx - fx + 1][gy - fy + 1][gz - fz + 1];	deconv3(g, gx, gy, gz, f, fx, fy, fz, h2);	printf("deconv3(g, f):\n");	for (int i = 0; i < gx - fx + 1; i++) {		for (int j = 0; j < gy - fy + 1; j++) {			for (int k = 0; k < gz - fz + 1; k++)				printf("%g ", h2[i][j][k]);			printf("\n");		}		if (i < gx - fx) printf("\n");	} 	double f2[gx - hx + 1][gy - hy + 1][gz - hz + 1];	deconv3(g, gx, gy, gz, h, hx, hy, hz, f2);	printf("\ndeconv3(g, h):\n");	for (int i = 0; i < gx - hx + 1; i++) {		for (int j = 0; j < gy - hy + 1; j++) {			for (int k = 0; k < gz - hz + 1; k++)				printf("%g ", f2[i][j][k]);			printf("\n");		}		if (i < gx - hx) printf("\n");	}} /* two-D case; since task doesn't require showing it, it's commented out *//*int main(){	PI = atan2(1,1) * 4;	double h[][6] = { 	{-8, 1, -7, -2, -9, 4},				{4, 5, -5, 2, 7, -1},				{-6, -3, -3, -6, 9, 5} };	int hr = 3, hc = 6; 	double f[][5] = {	{-5, 2, -2, -6, -7},				{9, 7, -6, 5, -7},				{1, -1, 9, 2, -7},				{5, 9, -9, 2, -5},				{-8, 5, -2, 8, 5} };	int fr = 5, fc = 5;	double g[][10] = {			{40, -21, 53, 42, 105, 1, 87, 60, 39, -28},			{-92, -64, 19, -167, -71, -47, 128, -109, 40, -21},			{58, 85, -93, 37, 101, -14, 5, 37, -76, -56},			{-90, -135, 60, -125, 68, 53, 223, 4, -36, -48},			{78, 16, 7, -199, 156, -162, 29, 28, -103, -10},			{-62, -89, 69, -61, 66, 193, -61, 71, -8, -30},			{48, -6, 21, -9, -150, -22, -56, 32, 85, 25}	};	int gr = 7, gc = 10; 	double h2[gr - fr + 1][gc - fc + 1];	deconv2(g, gr, gc, f, fr, fc, h2);	for (int i = 0; i < gr - fr + 1; i++) {		for (int j = 0; j < gc - fc + 1; j++)			printf(" %g", h2[i][j]);		printf("\n");	} 	double f2[gr - hr + 1][gc - hc + 1];	deconv2(g, gr, gc, h, hr, hc, f2);	for (int i = 0; i < gr - hr + 1; i++) {		for (int j = 0; j < gc - hc + 1; j++)			printf(" %g", f2[i][j]);		printf("\n");	}}*/
Output
deconv3(g, f):-6 -8 -5 9 -7 9 -6 -8 2 -7 9 8  7 4 4 -6 9 9 4 -4 -3 7 -2 -3  deconv3(g, h):-9 5 -8 3 5 1  -1 -7 2 -5 -6 6  8 5 8 -2 -6 -4

import std.stdio, std.conv, std.algorithm, std.numeric, std.range; class M(T) {    private size_t[] dim;    private size_t[] subsize;    private T[] d;     this(size_t[] dimension...) pure nothrow {        setDimension(dimension);        d[] = 0; // init each  entry to zero;    }     M!T dup() {        auto m = new M!T(dim);        return m.set1DArray(d);    }     M!T setDimension(size_t[] dimension ...) pure nothrow {        foreach (const e; dimension)            assert(e > 0, "no zero dimension");        dim = dimension.dup;        subsize = dim.dup;        foreach (immutable i; 0 .. dim.length)            subsize[i] = reduce!q{a * b}(1, dim[i + 1 .. $]); immutable dlength = dim[0] * subsize[0]; if (d.length != dlength) d = new T[dlength]; return this; } M!T set1DArray(in T[] t ...) pure nothrow @nogc { auto minLen = min(t.length, d.length); d[] = 0; d[0 .. minLen] = t[0 .. minLen]; return this; } size_t[] seq2idx(in size_t seq) const pure nothrow { size_t acc = seq, tmp; size_t[] idx; foreach (immutable e; subsize) { idx ~= tmp = acc / e; acc = acc - tmp * e; // same as % (mod) e. } return idx; } size_t size() const pure nothrow @nogc @property { return d.length; } size_t rank() const pure nothrow @nogc @property { return dim.length; } size_t[] shape() const pure nothrow @property { return dim.dup; } T[] raw() const pure nothrow @property { return d.dup; } bool checkBound(size_t[] idx ...) const pure nothrow @nogc { if (idx.length > dim.length) return false; foreach (immutable i, immutable dm; idx) if (dm >= dim[i]) return false; return true; } T opIndex(size_t[] idx ...) const pure nothrow @nogc { assert(checkBound(idx), "OOPS"); return d[dotProduct(idx, subsize)]; } T opIndexAssign(T v, size_t[] idx ...) pure nothrow @nogc { assert(checkBound(idx), "OOPS"); d[dotProduct(idx, subsize)] = v; return v; } override bool opEquals(Object o) const pure { const rhs = to!(M!T)(o); return dim == rhs.dim && d == rhs.d; } int opApply(int delegate(ref size_t[]) dg) const { size_t[] yieldIdx; foreach (immutable i; 0 .. d.length) { yieldIdx = seq2idx(i); if (dg(yieldIdx)) break; } return 0; } int opApply(int delegate(ref size_t[], ref T) dg) { size_t idx1d = 0; foreach (idx; this) { if (dg(idx, d[idx1d++])) break; } return 0; } // _this_ is h, rhs is f, output g. M!T convolute(M!T rhs) const pure nothrow { auto dm = dim.dup; dm[] += rhs.dim[] - 1; M!T m = new M!T(dm); // dm will be reused as m's idx. auto bound = m.size; foreach (immutable i; 0 .. d.length) { auto thisIdx = seq2idx(i); foreach (immutable j; 0 .. rhs.d.length) { dm[] = thisIdx[] + rhs.seq2idx(j)[]; immutable midx1d = dotProduct(dm, m.subsize); if (midx1d < bound) m.d[midx1d] += d[i] * rhs.d[j]; else break; // Bound reach, OK to break. } } return m; } // _this_ is g, rhs is f, output is h. M!T deconvolute(M!T rhs) const pure nothrow { auto dm = dim.dup; foreach (i, e; dm) assert(e + 1 > rhs.dim[i], "deconv : dimensions is zero or negative"); dm[] -= (rhs.dim[] - 1); auto m = new M!T(dm); // dm will be reused as rhs' idx. foreach (immutable i; 0 .. m.size) { auto idx = m.seq2idx(i); m.d[i] = this[idx]; foreach (immutable j; 0 .. i) { immutable jdx = m.seq2idx(j); dm[] = idx[] - jdx[]; if (rhs.checkBound(dm)) m.d[i] -= m.d[j] * rhs[dm]; } m.d[i] /= rhs.d[0]; } return m; } override string toString() const pure { return d.text; }} auto fold(T)(T[] arr, ref size_t[] d) pure { if (d.length == 0) d ~= arr.length; static if (is(T U : U[])) { // Is arr an array of arrays? assert(arr.length > 0, "no empty dimension"); d ~= arr[0].length; foreach (e; arr) assert(e.length == arr[0].length, "Not rectangular"); return fold(arr.reduce!q{a ~ b}, d); } else { assert(arr.length == d.reduce!q{a * b}, "Not same size"); return arr; }} auto arr2M(T)(T a) pure { size_t[] dm; auto d = fold(a, dm); alias E = ElementType!(typeof(d)); auto m = new M!E(dm); m.set1DArray(d); return m;} void main() { alias Mi = M!int; auto hh = [[[-6, -8, -5, 9], [-7, 9, -6, -8], [2, -7, 9, 8]], [[7, 4, 4, -6], [9, 9, 4, -4], [-3, 7, -2, -3]]]; auto ff = [[[-9, 5, -8], [3, 5, 1]],[[-1, -7, 2], [-5, -6, 6]], [[8, 5, 8],[-2, -6, -4]]]; auto h = arr2M(hh); auto f = arr2M(ff); const g = h.convolute(f); writeln("g == f convolute h ? ", g == f.convolute(h)); writeln("h == g deconv f ? ", h == g.deconvolute(f)); writeln("f == g deconv h ? ", f == g.deconvolute(h)); writeln(" f = ", f); writeln("g deconv h = ", g.deconvolute(h));} todo(may be not :): pretty print & convert to normal D array Output: g == f convolute h ? true h == g deconv f ? true f == g deconv h ? true f = [-9, 5, -8, 3, 5, 1, -1, -7, 2, -5, -6, 6, 8, 5, 8, -2, -6, -4] g deconv h = [-9, 5, -8, 3, 5, 1, -1, -7, 2, -5, -6, 6, 8, 5, 8, -2, -6, -4] ## Go Translation of: C package main import ( "fmt" "math" "math/cmplx") func fft(buf []complex128, n int) { out := make([]complex128, n) copy(out, buf) fft2(buf, out, n, 1)} func fft2(buf, out []complex128, n, step int) { if step < n { fft2(out, buf, n, step*2) fft2(out[step:], buf[step:], n, step*2) for j := 0; j < n; j += 2 * step { fj, fn := float64(j), float64(n) t := cmplx.Exp(-1i*complex(math.Pi, 0)*complex(fj, 0)/complex(fn, 0)) * out[j+step] buf[j/2] = out[j] + t buf[(j+n)/2] = out[j] - t } }} /* pad slice length to power of two */func padTwo(g []float64, le int, ns *int) []complex128 { n := 1 if *ns != 0 { n = *ns } else { for n < le { n *= 2 } } buf := make([]complex128, n) for i := 0; i < le; i++ { buf[i] = complex(g[i], 0) } *ns = n return buf} func deconv(g []float64, lg int, f []float64, lf int, out []float64, rowLe int) { ns := 0 g2 := padTwo(g, lg, &ns) f2 := padTwo(f, lf, &ns) fft(g2, ns) fft(f2, ns) h := make([]complex128, ns) for i := 0; i < ns; i++ { h[i] = g2[i] / f2[i] } fft(h, ns) for i := 0; i < ns; i++ { if math.Abs(real(h[i])) < 1e-10 { h[i] = 0 } } for i := 0; i > lf-lg-rowLe; i-- { out[-i] = real(h[(i+ns)%ns] / 32) }} func unpack2(m [][]float64, rows, le, toLe int) []float64 { buf := make([]float64, rows*toLe) for i := 0; i < rows; i++ { for j := 0; j < le; j++ { buf[i*toLe+j] = m[i][j] } } return buf} func pack2(buf []float64, rows, fromLe, toLe int, out [][]float64) { for i := 0; i < rows; i++ { for j := 0; j < toLe; j++ { out[i][j] = buf[i*fromLe+j] / 4 } }} func deconv2(g [][]float64, rowG, colG int, f [][]float64, rowF, colF int, out [][]float64) { g2 := unpack2(g, rowG, colG, colG) f2 := unpack2(f, rowF, colF, colG) ff := make([]float64, (rowG-rowF+1)*colG) deconv(g2, rowG*colG, f2, rowF*colG, ff, colG) pack2(ff, rowG-rowF+1, colG, colG-colF+1, out)} func unpack3(m [][][]float64, x, y, z, toY, toZ int) []float64 { buf := make([]float64, x*toY*toZ) for i := 0; i < x; i++ { for j := 0; j < y; j++ { for k := 0; k < z; k++ { buf[(i*toY+j)*toZ+k] = m[i][j][k] } } } return buf} func pack3(buf []float64, x, y, z, toY, toZ int, out [][][]float64) { for i := 0; i < x; i++ { for j := 0; j < toY; j++ { for k := 0; k < toZ; k++ { out[i][j][k] = buf[(i*y+j)*z+k] / 4 } } }} func deconv3(g [][][]float64, gx, gy, gz int, f [][][]float64, fx, fy, fz int, out [][][]float64) { g2 := unpack3(g, gx, gy, gz, gy, gz) f2 := unpack3(f, fx, fy, fz, gy, gz) ff := make([]float64, (gx-fx+1)*gy*gz) deconv(g2, gx*gy*gz, f2, fx*gy*gz, ff, gy*gz) pack3(ff, gx-fx+1, gy, gz, gy-fy+1, gz-fz+1, out)} func main() { f := [][][]float64{ {{-9, 5, -8}, {3, 5, 1}}, {{-1, -7, 2}, {-5, -6, 6}}, {{8, 5, 8}, {-2, -6, -4}}, } fx, fy, fz := len(f), len(f[0]), len(f[0][0]) g := [][][]float64{ {{54, 42, 53, -42, 85, -72}, {45, -170, 94, -36, 48, 73}, {-39, 65, -112, -16, -78, -72}, {6, -11, -6, 62, 49, 8}}, {{-57, 49, -23, 52, -135, 66}, {-23, 127, -58, -5, -118, 64}, {87, -16, 121, 23, -41, -12}, {-19, 29, 35, -148, -11, 45}}, {{-55, -147, -146, -31, 55, 60}, {-88, -45, -28, 46, -26, -144}, {-12, -107, -34, 150, 249, 66}, {11, -15, -34, 27, -78, -50}}, {{56, 67, 108, 4, 2, -48}, {58, 67, 89, 32, 32, -8}, {-42, -31, -103, -30, -23, -8}, {6, 4, -26, -10, 26, 12}, }, } gx, gy, gz := len(g), len(g[0]), len(g[0][0]) h := [][][]float64{ {{-6, -8, -5, 9}, {-7, 9, -6, -8}, {2, -7, 9, 8}}, {{7, 4, 4, -6}, {9, 9, 4, -4}, {-3, 7, -2, -3}}, } hx, hy, hz := gx-fx+1, gy-fy+1, gz-fz+1 h2 := make([][][]float64, hx) for i := 0; i < hx; i++ { h2[i] = make([][]float64, hy) for j := 0; j < hy; j++ { h2[i][j] = make([]float64, hz) } } deconv3(g, gx, gy, gz, f, fx, fy, fz, h2) fmt.Println("deconv3(g, f):\n") for i := 0; i < hx; i++ { for j := 0; j < hy; j++ { for k := 0; k < hz; k++ { fmt.Printf("% .10g ", h2[i][j][k]) } fmt.Println() } if i < hx-1 { fmt.Println() } } kx, ky, kz := gx-hx+1, gy-hy+1, gz-hz+1 f2 := make([][][]float64, kx) for i := 0; i < kx; i++ { f2[i] = make([][]float64, ky) for j := 0; j < ky; j++ { f2[i][j] = make([]float64, kz) } } deconv3(g, gx, gy, gz, h, hx, hy, hz, f2) fmt.Println("\ndeconv(g, h):\n") for i := 0; i < kx; i++ { for j := 0; j < ky; j++ { for k := 0; k < kz; k++ { fmt.Printf("% .10g ", f2[i][j][k]) } fmt.Println() } if i < kx-1 { fmt.Println() } }} Output: deconv3(g, f): -6 -8 -5 9 -7 9 -6 -8 2 -7 9 8 7 4 4 -6 9 9 4 -4 -3 7 -2 -3 deconv(g, h): -9 5 -8 3 5 1 -1 -7 2 -5 -6 6 8 5 8 -2 -6 -4  ## J Actually it is a matter of setting up the linear equations and then solving them. Implementation: deconv3 =: 4 : 0 sz =. x >:@-&$ y                                      NB. shape of z poi =.  ,<"1 ($y) ,"0/&(,@i.) sz NB. pair of indexes t=. /: sc=: , <@(+"1)/&(#: ,@i.)/ ($y),:sz             NB. order of ,y T0=. (<"0,x) ,:~ (]/:"1 {.)&.> (<, y) ({:@] ,: ({"1~ {.))&.>  sc <@|:@:>/.&(t&{) poi   NB. set of boxed equations T1=. (,x),.~(<0 #~ */sz) (({:@])({[email protected]])[})&> {.T0     NB. set of linear equations sz $1e_8 round ({:"1 %. }:"1) T1)round=: [ * <[email protected]%~ Data: h1=: _8 2 _9 _2 9 _8 _2f1=: 6 _9 _7 _5g1=: _48 84 _16 95 125 _70 7 29 54 10 h2=: ".;._2]0 :0 _8 1 _7 _2 _9 4 4 5 _5 2 7 _1 _6 _3 _3 _6 9 5) f2=: ".;._2]0 :0 _5 2 _2 _6 _7 9 7 _6 5 _7 1 _1 9 2 _7 5 9 _9 2 _5 _8 5 _2 8 5) g2=: ".;._2]0 :0 40 _21 53 42 105 1 87 60 39 _28 _92 _64 19 _167 _71 _47 128 _109 40 _21 58 85 _93 37 101 _14 5 37 _76 _56 _90 _135 60 _125 68 53 223 4 _36 _48 78 16 7 _199 156 _162 29 28 _103 _10 _62 _89 69 _61 66 193 _61 71 _8 _30 48 _6 21 _9 _150 _22 _56 32 85 25) h3=: ".;._1;._2]0 :0/ _6 _8 _5 9/ _7 9 _6 _8/ 2 _7 9 8/ 7 4 4 _6/ 9 9 4 _4/ _3 7 _2 _3) f3=: ".;._1;._2]0 :0/ _9 5 _8/ 3 5 1/ _1 _7 2/ _5 _6 6/ 8 5 8/_2 _6 _4) g3=: ".;._2;._1]0 :0 / 54 42 53 _42 85 _72 45 _170 94 _36 48 73 _39 65 _112 _16 _78 _72 6 _11 _6 62 49 8/ _57 49 _23 52 _135 66 _23 127 _58 _5 _118 64 87 _16 121 23 _41 _12 _19 29 35 _148 _11 45/ _55 _147 _146 _31 55 60 _88 _45 _28 46 _26 _144 _12 _107 _34 150 249 66 11 _15 _34 27 _78 _50 / 56 67 108 4 2 _48 58 67 89 32 32 _8 _42 _31 _103 _30 _23 _8 6 4 _26 _10 26 12) Tests:  h1 -: g1 deconv3 f11 h2 -: g2 deconv3 f21 h3 -: g3 deconv3 f3 NB. -: checks for matching structure and data1 ## Mathematica / Wolfram Language Round[ListDeconvolve[{6, -9, -7, -5}, {-48, 84, -16, 95, 125, -70, 7, 29, 54, 10}, Method -> "Wiener"]] Round[ListDeconvolve[{{-5, 2, -2, -6, -7}, {9, 7, -6, 5, -7}, {1, -1, 9, 2, -7}, {5, 9, -9, 2, -5}, {-8, 5, -2, 8, 5}}, {{40, -21, 53, 42, 105, 1, 87, 60, 39, -28}, {-92, -64, 19, -167, -71, -47, 128, -109, 40, -21}, {58, 85, -93, 37, 101, -14, 5, 37, -76, -56}, {-90, -135, 60, -125, 68, 53, 223, 4, -36, -48}, {78, 16, 7, -199, 156, -162, 29, 28, -103, -10}, {-62, -89, 69, -61, 66, 193, -61, 71, -8, -30}, {48, -6, 21, -9, -150, -22, -56, 32, 85, 25}}, Method -> "Wiener"]] Round[ListDeconvolve [{{{-9, 5, -8}, {3, 5, 1}}, {{-1, -7, 2}, {-5, -6, 6}}, {{8, 5, 8}, {-2, -6, -4}}}, {{{54, 42, 53, -42, 85, -72}, {45, -170, 94, -36, 48, 73}, {-39, 65, -112, -16, -78, -72}, {6, -11, -6, 62, 49, 8}}, {{-57, 49, -23, 52, -135, 66}, {-23, 127, -58, -5, -118, 64}, {87, -16, 121, 23, -41, -12}, {-19, 29, 35, -148, -11, 45}}, {{-55, -147, -146, -31, 55, 60}, {-88, -45, -28, 46, -26, -144}, {-12, -107, -34, 150, 249, 66}, {11, -15, -34, 27, -78, -50}}, {{56, 67, 108, 4, 2, -48}, {58, 67, 89, 32, 32, -8}, {-42, -31, -103, -30, -23, -8}, {6, 4, -26, -10, 26, 12}}}, Method -> "Wiener"]] Output: The built-in ListDeconvolve function pads output to the same dimensions as the original data... {-8, 2, -9, -2, 9, -8, -2, 0, 0, 0} {{0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, -8, 1, -7, -2, -9, 4, 0, 0}, {0, 0, 4, 5, -5, 2, 7, -1, 0, 0}, {0, 0, -6, -3, -3, -6, 9, 5, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}} {{{0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0}}, {{0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0}}, {{-6, -8, -5, 9, 0, 0}, {-7, 9, -6, -8, 0, 0}, {2, -7, 9, 8, 0, 0}, {0, 0, 0, 0, 0, 0}}, {{7, 4, 4, -6, 0, 0}, {9, 9, 4, -4, 0, 0}, {-3, 7, -2, -3, 0, 0}, {0, 0, 0, 0, 0, 0}}} ## Perl Library: ntheory Translation of: Perl 6 use feature 'say';use ntheory qw/forsetproduct/; # Deconvolution of N dimensional matricessub deconvolve_N { our @g; local *g = shift; our @f; local *f = shift; my @df = shape(@f); my @dg = shape(@g); my @hsize; push @hsize,$dg[$_] -$df[$_] + 1 for 0..$#df;    my @toSolve = map { [row(\@g, \@f, \@hsize, $_)] } coords(shape(@g)); rref( \@toSolve ); my @h; my$n = 0;    for (coords(@hsize)) {        my($k,$j,$i) = split ' ',$_;        $h[$i][$j][$k] = $toSolve[$n++][-1];    }    @h;} sub row {    our @g;      local *g      = shift;    our @f;      local *f      = shift;    our @hsize;  local *hsize  = shift;    my @gc = reverse split ' ', shift;     my @row;    my @fdim = shape(@f);    for (coords(@hsize)) {        my @hc = reverse split ' ', $_; my @fc; for my$i (0..$#hc) { my$window = $gc[$i] - $hc[$i];            push(@fc, $window), next if 0 <=$window && $window <$fdim[$i]; } push @row,$#fc == $#hc ?$f [$fc[0]] [$fc[1]] [$fc[2]] : 0; } push @row,$g [$gc[0]] [$gc[1]] [$gc[2]]; return @row;} sub rref { our @m; local *m = shift; @m or return; my ($lead, $rows,$cols) = (0, scalar(@m), scalar(@{$m[0]})); foreach my$r (0 .. $rows - 1) {$lead < $cols or return; my$i = $r; until ($m[$i][$lead])         {++$i ==$rows or next;          $i =$r;          ++$lead ==$cols and return;}       @m[$i,$r] = @m[$r,$i];      my $lv =$m[$r][$lead];      $_ /=$lv foreach @{ $m[$r] };       my @mr = @{ $m[$r] };      foreach my $i (0 ..$rows - 1)         {$i ==$r and next;          ($lv, my$n) = ($m[$i][$lead], -1);$_ -= $lv *$mr[++$n] foreach @{$m[$i] };} ++$lead;}} # Constructs an AoA of coordinates to all elements of N dimensional arraysub coords {    my(@dimensions) = reverse @_;    my(@ranges,@coords);    push @ranges, [0..$_-1] for @dimensions; forsetproduct { push @coords, "@_" } @ranges; @coords;} sub shape { my(@dim); push @dim, scalar @_; push @dim, shape(@{$_[0]}) if 'ARRAY' eq ref $_[0]; @dim;} # Pretty printer for N dimensional arrays# Assumes if first element in level is an array, then all aresub pretty_print { my($i, @a) = @_;    if ('ARRAY' eq ref $a[0]) { say ' 'x$i, '[';        pretty_print($i+2, @$_) for @a;        say ' 'x$i, ']',$i ? ',' : '';    } else {        say ' 'x$i, '[', sprintf("@{['%5s'[email protected]]}",@a), ']',$i ? ',' : '';    }} my @f = (  [    [ -9,  5, -8 ],    [  3,  5,  1 ],  ],  [    [ -1, -7,  2 ],    [ -5, -6,  6 ],  ],  [    [  8,  5,  8 ],    [ -2, -6, -4 ],  ]); my @g = (  [    [  54,  42,  53, -42,  85, -72 ],    [  45,-170,  94, -36,  48,  73 ],    [ -39,  65,-112, -16, -78, -72 ],    [   6, -11,  -6,  62,  49,   8 ],  ],  [    [ -57,  49, -23,  52,-135,  66 ],    [ -23, 127, -58,  -5,-118,  64 ],    [  87, -16, 121,  23, -41, -12 ],    [ -19,  29,  35,-148, -11,  45 ],  ],  [    [ -55,-147,-146, -31,  55,  60 ],    [ -88, -45, -28,  46, -26,-144 ],    [ -12,-107, -34, 150, 249,  66 ],    [  11, -15, -34,  27, -78, -50 ],  ],  [    [  56,  67, 108,   4,   2, -48 ],    [  58,  67,  89,  32,  32,  -8 ],    [ -42, -31,-103, -30, -23,  -8 ],    [   6,   4, -26, -10,  26,  12 ],  ]); my @h  = deconvolve_N( \@g, \@f );my @ff = deconvolve_N( \@g, \@h ); my $d = scalar shape(@g);print "${d}D arrays:\n";print "h =\n";pretty_print(0,@h);print "\nff =\n";pretty_print(0,@ff);
Output:
3D arrays:
h =
[
[
[   -6   -8   -5    9],
[   -7    9   -6   -8],
[    2   -7    9    8],
],
[
[    7    4    4   -6],
[    9    9    4   -4],
[   -3    7   -2   -3],
],
]

ff =
[
[
[   -9    5   -8],
[    3    5    1],
],
[
[   -1   -7    2],
[   -5   -6    6],
],
[
[    8    5    8],
[   -2   -6   -4],
],
]

## Perl 6

Works with Rakudo 2018.03.

Translation of Tcl.

# Deconvolution of N dimensional matrices.sub deconvolve-N ( @g, @f ) {    my @hsize = @g.shape »-« @f.shape »+» 1;     my @toSolve = coords(@g.shape).map:      { [row(@g, @f, $^coords, @hsize)] }; my @solved = rref( @toSolve ); my @h; for flat coords(@hsize) Z @solved[*;*-1] ->$_, $v { @h.AT-POS(|$_) = $v; } return @h;} # Construct a row for each value in @g to be sent to the simultaneous equation solversub row ( @g, @f, @gcoord,$hsize ) {    my @row;    @gcoord = @gcoord[(^@f.shape)]; # clip extraneous values    for coords( $hsize ) -> @hc { my @fcoord; for ^@hc ->$i {            my $window = @gcoord[$i] - @hc[$i]; @fcoord.push($window) and next if 0 <= $window < @f.shape[$i];            last;        }        @row.push: @fcoord == @hc ?? @f.AT-POS(|@fcoord) !! 0;    }    @row.push: @g.AT-POS(|@gcoord);    return @row;} # Constructs an AoA of coordinates to all elements of N dimensional arraysub coords ( @dim ) {    @[reverse $_ for [X] ([^$_] for reverse @dim)];} # Reduced Row Echelon Form simultaneous equation solver# Can handle over-specified systems (N unknowns in N + M equations)sub rref (@m) {    return unless @m;    my ($lead,$rows, $cols) = 0, +@m, +@m[0]; # Trim off over specified rows if they exist, for efficiency if$rows >= $cols { @m = trim_system(@m);$rows = +@m;    }     for ^$rows ->$r {        $lead <$cols or return @m;        my $i =$r;        until @m[$i;$lead] {            ++$i ==$rows or next;            $i =$r;            ++$lead ==$cols and return @m;        }        @m[$i,$r] = @m[$r,$i] if $r !=$i;        my $lv = @m[$r;$lead]; @m[$r] »/=» $lv; for ^$rows -> $n { next if$n == $r; @m[$n] »-=» @m[$r] »*» (@m[$n;$lead] // 0); } ++$lead;    }    return @m;     # Reduce a system of equations to N equations with N unknowns    sub trim_system ($m) { my ($vars, @t) = +$m[0]-1; for ^$vars -> $lead { for ^$m -> $row { @t.push: |$m.splice( $row, 1 ) and last if$m[$row;$lead];            }        }        while (+@t < $vars) and +$m { @t.push: $m.splice(0, 1) }; return @t; }} # Pretty printer for N dimensional arrays# Assumes if first element in level is an array, then all aresub pretty_print ( @array,$indent = 0 ) {    if @array[0] ~~ Array {        say ' ' x $indent,"["; pretty_print($_, $indent + 2 ) for @array; say ' ' x$indent, "]{$indent??','!!''}"; } else { say ' ' x$indent, "[{say_it(@array)} ]{$indent??','!!''}"; } sub say_it ( @array ) { return join ",", @array».fmt("%4s"); }} my @f[3;2;3] = ( [ [ -9, 5, -8 ], [ 3, 5, 1 ], ], [ [ -1, -7, 2 ], [ -5, -6, 6 ], ], [ [ 8, 5, 8 ], [ -2, -6, -4 ], ]); my @g[4;4;6] = ( [ [ 54, 42, 53, -42, 85, -72 ], [ 45,-170, 94, -36, 48, 73 ], [ -39, 65,-112, -16, -78, -72 ], [ 6, -11, -6, 62, 49, 8 ], ], [ [ -57, 49, -23, 52,-135, 66 ], [ -23, 127, -58, -5,-118, 64 ], [ 87, -16, 121, 23, -41, -12 ], [ -19, 29, 35,-148, -11, 45 ], ], [ [ -55,-147,-146, -31, 55, 60 ], [ -88, -45, -28, 46, -26,-144 ], [ -12,-107, -34, 150, 249, 66 ], [ 11, -15, -34, 27, -78, -50 ], ], [ [ 56, 67, 108, 4, 2, -48 ], [ 58, 67, 89, 32, 32, -8 ], [ -42, -31,-103, -30, -23, -8 ], [ 6, 4, -26, -10, 26, 12 ], ]); say "# {[email protected]}D array:";my @h = deconvolve-N( @g, @f );say "h =";pretty_print( @h );my @h-shaped[2;3;4] = @(deconvolve-N( @g, @f ));my @ff = deconvolve-N( @g, @h-shaped );say "\nff =";pretty_print( @ff ); Output: # 3D array: h = [ [ [ -6, -8, -5, 9 ], [ -7, 9, -6, -8 ], [ 2, -7, 9, 8 ], ], [ [ 7, 4, 4, -6 ], [ 9, 9, 4, -4 ], [ -3, 7, -2, -3 ], ], ] ff = [ [ [ -9, 5, -8 ], [ 3, 5, 1 ], ], [ [ -1, -7, 2 ], [ -5, -6, 6 ], ], [ [ 8, 5, 8 ], [ -2, -6, -4 ], ], ] ## Phix Translation of: Tcl Quite frankly I'm fairly astonished that it actually works... (be warned this contains an exciting mix of 0- and 1- based indexes) -- demo\rosetta\Deconvolution.exw function m_size(sequence m)---- returns the size of a matrix as a list of lengths-- sequence res = {} object me = m while sequence(me) do res &= length(me) me = me[1] end while return resend function function product(sequence s)---- multiply all elements of s together-- integer res = s[1] for i=2 to length(s) do res *= s[i] end for return resend function function make_coordset(sequence size)---- returns all points in the matrix, in zero-based indexes, -- eg {{0,0,0}..{3,3,5}} for a 4x4x6 matrix [96 in total]-- sequence res = {} integer count = product(size) for i=0 to count-1 do sequence coords = {} integer j = i for s=length(size) to 1 by -1 do integer dimension = size[s] coords &= mod(j,dimension) j = floor(j/dimension) end for coords = reverse(coords) res = append(res,coords) end for return resend function function row(sequence g, f, gs, gc, fs, hs)----# Assembles a row, which is one of the simultaneous equations that needs--# to be solved by reducing the whole set to reduced row echelon form. Note--# that each row describes the equation for a single cell of the 'g' function.--#--# Arguments:--# g The "result" matrix of the convolution being undone.--# h The known "input" matrix of the convolution being undone.--# gs The size descriptor of 'g', passed as argument for efficiency.--# gc The coordinate in 'g' that we are generating the equation for.--# fs The size descriptor of 'f', passed as argument for efficiency.--# hs The size descriptor of 'h' (the unknown "input" matrix), passed--# as argument for efficiency.-- sequence row = {}, coords = make_coordset(hs) for i=1 to length(coords) do sequence hc = coords[i] object fn = f for k=1 to length(gc) do integer d = gc[k]-hc[k] if d<0 or d>=fs[k] then fn = 0 exit end if fn = fn[d+1] end for row = append(row,fn) end for object gn = g for i=1 to length(gc) do gn = gn[gc[i]+1] end for row = append(row,gn) return rowend function function toRREF(sequence m)---- [renamed] copy of Reduced_row_echelon_form.htm#Phix-- plus one small tweak, as noted below, exit->return,-- not that said seems to make any actual difference.--integer lead = 1, rows = length(m), cols = length(m[1]) for r=1 to rows do if lead>=cols then exit end if integer i = r while m[i][lead]=0 do i += 1 if i=rows then i = r lead += 1-- if lead=cols then exit end if if lead=cols then return m end if end if end while -- nb m[i] is assigned before m[r], which matters when i=r: {m[r],m[i]} = {sq_div(m[i],m[i][lead]),m[r]} for j=1 to rows do if j!=r then m[j] = sq_sub(m[j],sq_mul(m[j][lead],m[r])) end if end for lead += 1 end for return mend function function lset(sequence h, sequence idx, object v)-- helper routine: store v somewhere deep inside h integer i1 = idx[1]+1 if length(idx)=1 then h[i1] = v else h[i1] = lset(h[i1],idx[2..$],v)    end if    return hend function function deconvolve(sequence g, f)----# Deconvolve a pair of matrixes. Solves for 'h' such that 'g = f convolve h'.--#--# Arguments:--# g     The matrix of data to be deconvolved.--# f     The matrix describing the convolution to be removed.--      -- Compute the sizes of the various matrixes involved.    sequence gsize = m_size(g),             fsize = m_size(f),             hsize = sq_add(sq_sub(gsize,fsize),1)     -- Prepare the set of simultaneous equations to solve    sequence toSolve = {},             coords = make_coordset(gsize)    for i=1 to length(coords) do        toSolve = append(toSolve,row(g,f,gsize,coords[i],fsize,hsize))    end for     -- Solve the equations    sequence solved = toRREF(toSolve)     -- Create a result matrix of the right size    object h = 0    for i=length(hsize) to 1 by -1 do        h = repeat(h,hsize[i])    end for     -- Fill the results from the equations into the result matrix    coords = make_coordset(hsize)    for i=1 to length(coords) do        h = lset(h,coords[i],solved[i][$]) end for return hend function constant f1 = { 6, -9, -7, -5}, g1 = {-48, 84, -16, 95, 125, -70, 7, 29, 54, 10}, h1 = {-8, 2, -9, -2, 9, -8, -2} if deconvolve(g1, f1)!=h1 then ?9/0 end ifif deconvolve(g1, h1)!=f1 then ?9/0 end if constant f2 = {{-5, 2,-2,-6,-7}, { 9, 7,-6, 5,-7}, { 1,-1, 9, 2,-7}, { 5, 9,-9, 2,-5}, {-8, 5,-2, 8, 5}}, g2 = {{ 40, -21, 53, 42, 105, 1, 87, 60, 39, -28}, {-92, -64, 19,-167, -71, -47, 128,-109, 40, -21}, { 58, 85,-93, 37, 101, -14, 5, 37, -76, -56}, {-90,-135, 60,-125, 68, 53, 223, 4, -36, -48}, { 78, 16, 7,-199, 156,-162, 29, 28,-103, -10}, {-62, -89, 69, -61, 66, 193, -61, 71, -8, -30}, { 48, -6, 21, -9,-150, -22, -56, 32, 85, 25}}, h2 = {{-8, 1,-7,-2,-9, 4}, { 4, 5,-5, 2, 7,-1}, {-6,-3,-3,-6, 9, 5}} if deconvolve(g2, f2)!=h2 then ?9/0 end ifif deconvolve(g2, h2)!=f2 then ?9/0 end if constant f3 = {{{-9, 5, -8}, { 3, 5, 1}}, {{-1, -7, 2}, {-5, -6, 6}}, {{ 8, 5, 8}, {-2, -6, -4}}}, g3 = {{{ 54, 42, 53, -42, 85, -72}, { 45,-170, 94, -36, 48, 73}, {-39, 65,-112, -16, -78, -72}, { 6, -11, -6, 62, 49, 8}}, {{-57, 49, -23, 52,-135, 66}, {-23, 127, -58, -5,-118, 64}, { 87, -16, 121, 23, -41, -12}, {-19, 29, 35,-148, -11, 45}}, {{-55,-147,-146, -31, 55, 60}, {-88, -45, -28, 46, -26,-144}, {-12,-107, -34, 150, 249, 66}, { 11, -15, -34, 27, -78, -50}}, {{ 56, 67, 108, 4, 2, -48}, { 58, 67, 89, 32, 32, -8}, {-42, -31,-103, -30, -23, -8}, { 6, 4, -26, -10, 26, 12}}}, h3 = {{{ -6, -8, -5, 9}, { -7, 9, -6, -8}, { 2, -7, 9, 8}}, {{ 7, 4, 4, -6}, { 9, 9, 4, -4}, { -3, 7, -2, -3}}} if deconvolve(g3, f3)!=h3 then ?9/0 end ifif deconvolve(g3, h3)!=f3 then ?9/0 end if ppOpt({pp_Nest,2,pp_IntFmt,"%3d"})pp(deconvolve(g3, f3))pp(deconvolve(g3, h3)) Output: {{{ -6, -8, -5, 9}, { -7, 9, -6, -8}, { 2, -7, 9, 8}}, {{ 7, 4, 4, -6}, { 9, 9, 4, -4}, { -3, 7, -2, -3}}} {{{ -9, 5, -8}, { 3, 5, 1}}, {{ -1, -7, 2}, { -5, -6, 6}}, {{ 8, 5, 8}, { -2, -6, -4}}}  The version shipped in demo\rosetta contains the full 5 test sets: note that 5D takes a minute or two to complete. ## Tcl The trick to doing this (without using a library to do all the legwork for you) is to recast the higher-order solutions into solutions in the 1D case. This is done by regarding an n-dimensional address as a coding of a 1-D address. package require Tcl 8.5namespace path {::tcl::mathfunc ::tcl::mathop} # Utility to extract the number of dimensions of a matrixproc rank m { for {set rank 0} {[llength$m] > 1} {incr rank} {	set m [lindex $m 0] } return$rank} # Utility to get the size of a matrix, as a list of lengthsproc size m {    set r [rank $m] set index {} set size {} for {set i 0} {$i<$r} {incr i} { lappend size [llength [lindex$m $index]] lappend index 0 } return$size} # Utility that iterates over the space of coordinates within a matrix.## Arguments:#   var   The name of the variable (in the caller's context) to set to each#         coordinate.#   size  The size of matrix whose coordinates are to be iterated over.#   body  The script to evaluate (in the caller's context) for each coordinate,#         with the variable named by 'var' set to the coordinate for the particular#         iteration.proc loopcoords {var size body} {    upvar 1 $var v set count [* {*}$size]    for {set i 0} {$i <$count} {incr i} {	set coords {}	set j $i for {set s$size} {[llength $s]} {set s [lrange$s 0 end-1]} {	    set dimension [lindex $s end] lappend coords [expr {$j % $dimension}] set j [expr {$j / $dimension}] } set v [lreverse$coords]	uplevel 1 $body }} # Assembles a row, which is one of the simultaneous equations that needs# to be solved by reducing the whole set to reduced row echelon form. Note# that each row describes the equation for a single cell of the 'g' function.## Arguments:# g The "result" matrix of the convolution being undone.# h The known "input" matrix of the convolution being undone.# gs The size descriptor of 'g', passed as argument for efficiency.# gc The coordinate in 'g' that we are generating the equation for.# fs The size descriptor of 'f', passed as argument for efficiency.# hs The size descriptor of 'h' (the unknown "input" matrix), passed# as argument for efficiency.proc row {g f gs gc fs hs} { loopcoords hc$hs {	set fc {}	set ok 1	foreach a $gc b$fs c $hc { set d [expr {$a - $c}] if {$d < 0 || $d >=$b} {		set ok 0		break	    }	    lappend fc $d } if {$ok} {	    lappend row [lindex $f$fc]	} else {	    lappend row 0	}    }    return [lappend row [lindex $g$gc]]} # Utility for converting a matrix to Reduced Row Echelon Form# From http://rosettacode.org/wiki/Reduced_row_echelon_form#Tclproc toRREF {m} {    set lead 0    set rows [llength $m] set cols [llength [lindex$m 0]]    for {set r 0} {$r <$rows} {incr r} {	if {$cols <=$lead} {	    break	}	set i $r while {[lindex$m $i$lead] == 0} {	    incr i	    if {$rows ==$i} {		set i $r incr lead if {$cols == $lead} { # Tcl can't break out of nested loops return$m		}	    }	}	# swap rows i and r	foreach j [list $i$r] row [list [lindex $m$r] [lindex $m$i]] {	    lset m $j$row	}	# divide row r by m(r,lead)	set val [lindex $m$r $lead] for {set j 0} {$j < $cols} {incr j} { lset m$r $j [/ [double [lindex$m $r$j]] $val] } for {set i 0} {$i < $rows} {incr i} { if {$i != $r} { # subtract m(i,lead) multiplied by row r from row i set val [lindex$m $i$lead]		for {set j 0} {$j <$cols} {incr j} {		    lset m $i$j \			[- [lindex $m$i $j] [*$val [lindex $m$r $j]]] } } } incr lead } return$m} # Deconvolve a pair of matrixes. Solves for 'h' such that 'g = f convolve h'.## Arguments:#   g     The matrix of data to be deconvolved.#   f     The matrix describing the convolution to be removed.#   type  Optional description of the type of data expected. Defaults to 32-bit#         integer data; use 'double' for floating-point data.proc deconvolve {g f {type int}} {    # Compute the sizes of the various matrixes involved.    set gsize [size $g] set fsize [size$f]    foreach gs $gsize fs$fsize {	lappend hsize [expr {$gs -$fs + 1}]    }     # Prepare the set of simultaneous equations to solve    set toSolve {}    loopcoords coords $gsize { lappend toSolve [row$g $f$gsize $coords$fsize $hsize] } # Solve the equations set solved [toRREF$toSolve]     # Make a dummy result matrix of the right size    set h 0    foreach hs [lreverse $hsize] {set h [lrepeat$hs $h]} # Fill the results from the equations into the result matrix set idx 0 loopcoords coords$hsize {	lset h $coords [$type [lindex $solved$idx end]]	incr idx    }     return $h} Demonstrating how to use for the 3-D case: # A pretty-printerproc pretty matrix { set size [rank$matrix]    if {$size == 1} { return $[join matrix ", "]$ } elseif {$size == 2} {	set out ""	foreach row $matrix { append out " " [pretty$row] ",\n"	}	return $[string trimleft [string trimright out ,\n]]$    }    set rowout {}    foreach row $matrix {append rowout [pretty$row] ,\n}    set rowout2 {}    foreach row [split [string trimright $rowout ,\n] \n] { append rowout2 " "$row \n    }    return $\n[string trimright rowout2 \n]\n$} # The 3D test dataset f {    {{-9 5 -8} {3 5 1}}     {{-1 -7 2} {-5 -6 6}}     {{8 5 8} {-2 -6 -4}}}set g {    {	{54 42 53 -42 85 -72} 	{45 -170 94 -36 48 73} 	{-39 65 -112 -16 -78 -72} 	{6 -11 -6 62 49 8}}     {	{-57 49 -23 52 -135 66} 	{-23 127 -58 -5 -118 64} 	{87 -16 121 23 -41 -12} 	{-19 29 35 -148 -11 45}}     {	{-55 -147 -146 -31 55 60} 	{-88 -45 -28 46 -26 -144} 	{-12 -107 -34 150 249 66} 	{11 -15 -34 27 -78 -50}}     {	{56 67 108 4 2 -48} 	{58 67 89 32 32 -8} 	{-42 -31 -103 -30 -23 -8} 	{6 4 -26 -10 26 12}}} # Now do the deconvolution and print it outputs h:\ [pretty [deconvolve $g$f]]

Output:

h: [
[[-6, -8, -5, 9],
[-7, 9, -6, -8],
[2, -7, 9, 8]],
[[7, 4, 4, -6],
[9, 9, 4, -4],
[-3, 7, -2, -3]]


## Ursala

This is done mostly with list operations that are either primitive or standard library functions in the language (e.g., zipp, zipt, and pad). The equations are solved by the dgelsd function from the Lapack library. The break function breaks a long list into a sequence of sublists according to a given template, and the band function is taken from the Deconvolution/1D solution.

#import std#import nat break = ~&r**+ zipt*+ ~&lh*~+ ~&lzyCPrX|\+ -*^|\~&tK33 :^/~& 0!*t band = pad0+ ~&rSS+ zipt^*D(~&r,^lrrSPT/~&ltK33tx zipt^/~&r ~&lSNyCK33+ zipp0)^/~&rx ~&B->NlNSPC ~&bt deconv = # takes a natural number n to the n-dimensional deconvolution function ~&?\math..div! iota; ~&!*; @h|\; (~&al^?\~&ar [email protected])^^(   ~&B->NlC~&bt*++ [email protected]+ ~~*,   lapack..dgelsd^^(      (~&||0.!**+ ~&B^?a\~&[email protected] ^lriFhNSS2iDrlYSK7LS2SL2rQ/~&alt [email protected])^|\~&+ gang,      @t =>~&l ~&[email protected]))

The equations tend to become increasingly sparse in higher dimensions, so the following alternative implementation uses the sparse matrix solver from the UMFPACK library instead of Lapack, which is also callable in Ursala, adjusted as shown for the different calling convention.

deconv = # takes a number n to the n-dimensional deconvolution function ~&?\math..div! iota; ~&!*; @h|\; -+   //+ ~&al^?\~&ar @alh2faltPrXPRX @liX ~&arr2arl2arrh3falrbt2XPRXlrhPCrtPCPNfallrrPXXPRCQNNCq,   ^^/-+~&B->NlC~&bt*+,[email protected],~~*+- (umf..di_a_trp^/~&[email protected] ^niK10mS/[email protected] ~&lnS)^^(      gang; ^|\~&; //+ -+         ^niK10/~& @NnmlSPASX ~&r->lL @lrmK2K8SmtPK20PPPX ^/~&rrnS2lC ~&[email protected],         num+ ~&B^?a\~&[email protected] -+            ~&l?\~&r *=r ~&K7LS+ * (*D ^\~&rr [email protected])^*D\~&r product^|/~& [email protected],            ^/~&alt @alh2faltPrDPMX -+               ~&rFS+ num*rSS+ zipt^*D/~&r ^lrrSPT/~&ltK33tx zipt^/~&r ~&lSNyCK33+ zipp0,               ^/~&rx ~&B->NlNSPC ~&bt+-+-+-,      @t =>~&l ~&[email protected])+-

UMFPACK doesn't solve systems with more equations than unknowns, so the system is pruned to a square matrix by first selecting an equation containing only a single variable, then selecting one from those remaining that contains only a single variable not already selected, and so on until all variables are covered, with any remaining unselected equations discarded. A random selection is made whenever there is a choice. This method will cope with larger data sets than feasible using dense and overdetermined matrices, but is less robust in the presence of noise. However, some improvement may be possible by averaging the results over several runs. Here is a test program.

h = <<<-6.,-8.,-5.,9.>,<-7.,9.,-6.,-8.>,<2.,-7.,9.,8.>>,<<7.,4.,4.,-6.>,<9.,9.,4.,-4.>,<-3.,7.,-2.,-3.>>>f = <<<-9.,5.,-8.>,<3.,5.,1.>>,<<-1.,-7.,2.>,<-5.,-6.,6.>>,<<8.,5.,8.>,<-2.,-6.,-4.>>> g = <   <      <54.,42.,53.,-42.,85.,-72.>,      <45.,-170.,94.,-36.,48.,73.>,      <-39.,65.,-112.,-16.,-78.,-72.>,      <6.,-11.,-6.,62.,49.,8.>>,   <      <-57.,49.,-23.,52.,-135.,66.>,      <-23.,127.,-58.,-5.,-118.,64.>,      <87.,-16.,121.,23.,-41.,-12.>,      <-19.,29.,35.,-148.,-11.,45.>>,   <      <-55.,-147.,-146.,-31.,55.,60.>,      <-88.,-45.,-28.,46.,-26.,-144.>,      <-12.,-107.,-34.,150.,249.,66.>,      <11.,-15.,-34.,27.,-78.,-50.>>,   <      <56.,67.,108.,4.,2.,-48.>,      <58.,67.,89.,32.,32.,-8.>,      <-42.,-31.,-103.,-30.,-23.,-8.>,      <6.,4.,-26.,-10.,26.,12.>>> #cast %eLLLm test = <   'h': deconv3(g,f),   'f': deconv3(g,h)>

output:

<
'h': <
<
<
-6.000000e+00,
-8.000000e+00,
-5.000000e+00,
9.000000e+00>,
<
-7.000000e+00,
9.000000e+00,
-6.000000e+00,
-8.000000e+00>,
<
2.000000e+00,
-7.000000e+00,
9.000000e+00,
8.000000e+00>>,
<
<
7.000000e+00,
4.000000e+00,
4.000000e+00,
-6.000000e+00>,
<
9.000000e+00,
9.000000e+00,
4.000000e+00,
-4.000000e+00>,
<
-3.000000e+00,
7.000000e+00,
-2.000000e+00,
-3.000000e+00>>>,
'f': <
<
<-9.000000e+00,5.000000e+00,-8.000000e+00>,
<3.000000e+00,5.000000e+00,1.000000e+00>>,
<
<-1.000000e+00,-7.000000e+00,2.000000e+00>,
<-5.000000e+00,-6.000000e+00,6.000000e+00>>,
<
<8.000000e+00,5.000000e+00,8.000000e+00>,
<-2.000000e+00,-6.000000e+00,-4.000000e+00>>>>